Building a Technology Recommender System Using Web Crawling and Natural Language Processing Technology

Abstract

Finding, retrieving, and processing information on technology from the Internet can be a tedious task. This article investigates if technological concepts such as web crawling and natural language processing are suitable means for knowledge discovery from unstructured information and the development of a technology recommender system by developing a prototype of such a system. It also analyzes how well the resulting prototype performs in regard to effectivity and efficiency. The research strategy based on design science research consists of four stages: (1) Awareness generation; (2) suggestion of a solution considering the information retrieval process; (3) development of an artefact in the form of a Python computer program; and (4) evaluation of the prototype within the scope of a comparative experiment. The evaluation yields that the prototype is highly efficient in retrieving basic and rather random extractive text summaries from websites that include the desired search terms. However, the effectivity, measured by the quality of results is unsatisfactory due to the aforementioned random arrangement of extracted sentences within the resulting summaries. It is found that natural language processing and web crawling are indeed suitable technologies for such a program whilst the use of additional technology/concepts would add significant value for a potential user. Several areas for incremental improvement of the prototype are identified.